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PaperclipPerfector  6mo ago (text post)   33575 thread views

Culture War Roundup for the week of November 4, 2024

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OpenAI Shifts Strategy to Slower, Smarter AI as GPT Scaling Limits Emerge, OpenAI's upcoming Orion model shows how GPT improvements are slowing down

Paywalled, but here's a summary from reddit:

"Some OpenAI employees who tested Orion report it achieved GPT-4-level performance after completing only 20% of its training, but the quality increase was smaller than the leap from GPT-3 to GPT-4, suggesting that traditional scaling improvements may be slowing as high-quality data becomes limited

  • Orion's training involved AI-generated data from previous models like GPT-4 and reasoning models, which may lead it to reproduce some behaviors of older models
  • OpenAI has created a "foundations" team to develop new methods for sustaining improvements as high-quality data supplies decrease
  • Orion's advanced code-writing features could raise operating costs in OpenAI's data centers, and running models like o1, estimated at six times the cost of simpler models, adds financial pressure to further scaling
  • OpenAI is finishing Orion's safety testing for a planned release early next year, which may break from the "GPT" naming convention to reflect changes in model development

“Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks, according to the employees. Orion performs better at language tasks but may not outperform previous models at tasks such as coding, according to an OpenAI employee. That could be a problem, as Orion may be more expensive for OpenAI to run in its data centers compared to other models it has recently released, one of those people said.”

This is one of several articles/posts/tweets coming out of the LLMsphere over the past couple of weeks that are renewing concerns over LLMs hitting diminishing returns.

Of course this is just speculation until OpenAI actually releases Orion (or whatever they end up calling it). And really we would need several models past Orion too to actually extrapolate a pattern. But this does fit with my subjective impression that the leap from GPT-3 to GPT-4 was not as big as the leap from GPT-2 to GPT-3, and the leap from 4 to o1 was not as big as the leap from 3 to 4. The fact that they're considering again releasing a new model without calling it GPT-5 is also telling. They know how psychologically important the "GPT-5" moniker has become at this point and they won't give that name to a model unless it really represents a major leap forward.

Speculation: It’s interesting that the bottleneck is given as lack of data rather than architecture. That opens up the possibility that we may be able to get things moving again by finding some other method of obtaining/creating useable data.

LLMs were historically created to use next-token-prediction as a means of solving natural language processing tasks. I think we can regard that problem as provisionally solved. When people talk about GPTs limits, they aren’t talking about its ability to take English input and produce readable English output. They are talking about general intelligence: the ability to output sensible, useful English output.

In short, LLMs are general learning machines using natural language as a proxy task. Natural language is cheap and information rich but any means of conveying information about the world is fair game, provided that it can be converted into the same token space that GPT is using using CLIP or something similar.

What is needed is large quantities of data that conveys causal information about the world. Video is probably a good place to start. Some kind of simulated self-play might also be useable. What else could be useable?

(I’m not sure how next-token prediction would work here)

In effect LLMS aren't smart, they are just great at recognizing patterns they are trained on. Google is great at recognizing text strings that it remembers, LLMS don't need matching strings they match on patterns and are able to combine patterns from multiple sources. LLMs aren't truly intelligent because they are dumbfounded if there isn't a good matching pattern in the training set. They are stumped in a way a human isn't if they encounter something new.

LLMs aren't going to replace humans because the set of all data is miniscule to the set of all potential patterns in the world.

LLMs aren't going to replace humans because the set of all data is miniscule to the set of all potential patterns in the world.

I mean, you can say LLMs aren't going to replace humans...but the 'potential patterns in the world' are all reducible to data in one way or another.

So some Machine trained on language AND physics data AND biology AND etc. etc. is still a potential contender, no?

I suppose it comes down to whether or not there is a ghost in the machine.

If human intelligence is all neurons that can be modeled as a graph with weighted edges then we should be able to simulate it.

Maybe we do that and still can’t get human intelligence to pop out of the simulated brain and find that something is missing.

It would be a bit funny if they design a machine that is provably a 1:1 simulation of a human brain, switch it on, and get an error message to the effect of "Cannot Execute Commands: This unit is not ensouled."

“Humunculus not installed: please refer to manual.”

I mean, that kind of sounds like you're saying it's provably not a 1:1 simulation of a human brain.

What you're describing is measurable evidence of new physics. Every physicist in the world would want to buy you a beer.

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